Under water motion tracking and monitoring using wireless sensor network and Machine learning

Gite, Pratik; Shrivastava, Anurag; Krishna, K. Murali; Kusumadevi, G.H.; Dilip, R; Potdar, Ravindra Manohar

doi:10.1016/j.matpr.2021.07.283

Cited by 13 publications

(3 citation statements)

References 33 publications

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“…The issue of mobility of underwater objects is addressed by using a statistical analysis approach to compute the next possible position of the node. Pratil et al [59] introduced a method for tracking and monitoring underwater movement through underwater wireless sensor networks and machine learning. It conducted simula-tions for two scenarios involving the random mobility of nodes.…”

Section: Motion Tracking and Monitoringmentioning

confidence: 99%

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Nkenyereye,

Ndibanje

2024

Electronics

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The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system.

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Section: Motion Tracking and Monitoringmentioning

confidence: 99%

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Nkenyereye,

Ndibanje

2024

Electronics

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“…Thus, WSNs are essential for getting context‐specific information and enabling decision‐making in the real world. In a WSN, a source node is a sensor using wireless technology; this sensor may collect data from the surrounding environment and transmit it to a central place known as the control station 16 . Alternative topologies may be employed since the WSN does not have any physical infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…In a WSN, a source node is a sensor using wireless technology; this sensor may collect data from the surrounding environment and transmit it to a central place known as the control station. 16 Alternative topologies may be employed since the WSN does not have any physical infrastructure. The most common topology for large network design is a tree-based or hierarchical one.…”

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confidence: 99%

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

Srivastava¹,

Paulus²

2022

Concurrency and Computation

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Nowadays, the wireless sensor network (WSN) with IoT is intended to monitor real-world physical or environmental phenomena in a number of applications, including foreign areas such as health and habitat monitoring. The WSN-IoT network generates huge volume of data, which has to be processed and accessed by the remote users. Due to this large volume of data generation and resource constraint ability make achieving optimal cluster based routing in WSN-IoT. The location of the sensor nodes significantly affects the accuracy of the information collected, which determines the quality of service provided by the application system. WSN can have multiple conflicts, which can create different coverage holes. These holes will break the existing overlap or connection and affect the required operation of the networks. Therefore, it is essential to find and repair the coverage holes to ensure the full functioning of the WSN as a motivation of this study. In this paper, we suggest a novel Coverage Hole aware Optimal Cluster based Routing (CHOCR) scheme for WSN-IoT. First, we propose Modified Lichtenberg optimization (MLO) algorithm for balanced clustering which improve the performance of coverage hole. Second, we develop a linear equilibrium optimization based decision making (LEO-DM) technique to subtract trust value of each IoT node using multiple restraints in cluster and consider the highest trusted node is act as cluster head (CH). After that, a hybrid deep recurrent neural network (HD-RNN) is developed for intermediate node selection to frame the routing between two nodes.Finally, we simulate our proposed CHOCR scheme on the NS3.26 simulator. According to energy consumption, network longevity, number of nodes that are still alive and packet delivery ratio, packet loss ratio and throughput, end-to-end latency and delay of our proposed CHOCR routing system, we compare it to other current routing schemes.

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Optimal sensor network routing with secure network monitoring using deep learning architectures

Qamar

2023

Neural Comput & Applic

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Under water motion tracking and monitoring using wireless sensor network and Machine learning

Cited by 13 publications

References 33 publications

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

Optimal sensor network routing with secure network monitoring using deep learning architectures

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